Memory serves as the pivotal nexus bridging past and future, providing both humans and AI systems with invaluable concepts and experience to navigate complex tasks. Recent research on autonomous agents has increasingly focused on designing efficient memory workflows by drawing on cognitive neuroscience. However, constrained by interdisciplinary barriers, existing works struggle to assimilate the essence of human memory mechanisms. To bridge this gap, we systematically synthesizes interdisciplinary knowledge of memory, connecting insights from cognitive neuroscience with LLM-driven agents. Specifically, we first elucidate the definition and function of memory along a progressive trajectory from cognitive neuroscience through LLMs to agents. We then provide a comparative analysis of memory taxonomy, storage mechanisms, and the complete management lifecycle from both biological and artificial perspectives. Subsequently, we review the mainstream benchmarks for evaluating agent memory. Additionally, we explore memory security from dual perspectives of attack and defense. Finally, we envision future research directions, with a focus on multimodal memory systems and skill acquisition.

翻译：记忆作为连接过去与未来的关键枢纽，为人类和人工智能系统提供了驾驭复杂任务所需的宝贵概念与经验。近年来，自主智能体的研究日益关注借鉴认知神经科学来设计高效记忆工作流。然而，受限于学科壁垒，现有研究难以充分吸收人类记忆机制的精髓。为弥合这一鸿沟，本文系统性地整合了跨学科的记忆知识，将认知神经科学的洞见与基于大语言模型的智能体相连接。具体而言，我们首先沿着从认知神经科学到大语言模型再到智能体的递进路径，阐明记忆的定义与功能。接着，我们从生物与人工双重视角，对记忆的分类体系、存储机制以及完整的管理生命周期进行了比较分析。随后，我们回顾了评估智能体记忆的主流基准。此外，我们从攻击与防御的双重角度探讨了记忆安全性。最后，我们展望了未来研究方向，重点关注多模态记忆系统与技能习得。